The ability to predict or explore polymorphism in organic crystal compounds is of great importance for many industries since many physical and chemical properties of molecular crystals strongly depend on the molecular packing in the solid state. Crystal structure prediction (CSP) aims to reduce the risk and improve the efficiency of pharmaceutical drug manufacturing by correctly predicting the low energy crystal forms of organic molecules from minimal information, such as the chemical diagram. At XtalPi, we have developed a cloud-based computational platform that combines advanced physics-based algorithms with A.I/machine learning algorithms. Our CSP platform can rapidly generate and rank millions of crystal structures for pharmaceutically relevant systems and we highlight some cases in which our CSP platform has revealed low energy polymorphs in challenging systems. We will also discuss some areas where we are expanding our computational platform to make accurate predictions on the physiochemical and pharmaceutical properties of small-molecule candidates for drug design, solid-form selection, and for other critical aspects of drug development.

Intelligent cloud-based algorithms for pharmaceutical drug development

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.